"""simple docstring"""



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()






class  snake_case__      (       unittest.TestCase       ):
 _snake_case       :					Any							  =						StableDiffusionLDMaDPipeline
 _snake_case       :					List[str]							  =						TEXT_TO_IMAGE_PARAMS
 _snake_case       :					Optional[int]							  =						TEXT_TO_IMAGE_BATCH_PARAMS
 _snake_case       :					Optional[Any]							  =						TEXT_TO_IMAGE_IMAGE_PARAMS


 def 						a__   ( self						):
        torch.manual_seed(0						)
        __a       =    UNetaDConditionModel(
            block_out_channels=(32, 64)    ,		layers_per_block=2    ,		sample_size=32    ,		in_channels=4    ,		out_channels=4    ,		down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")    ,		up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")    ,		cross_attention_dim=32    ,		)
        __a       =    DDIMScheduler(
            beta_start=0.0_0085    ,		beta_end=0.012    ,		beta_schedule="scaled_linear"    ,		clip_sample=lowerCamelCase    ,		set_alpha_to_one=lowerCamelCase    ,		)
        torch.manual_seed(0						)
        __a       =    AutoencoderKL(
            block_out_channels=[32, 64]    ,		in_channels=6    ,		out_channels=6    ,		down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]    ,		up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]    ,		latent_channels=4    ,		)
        torch.manual_seed(0						)
        __a       =    CLIPTextConfig(
            bos_token_id=0    ,		eos_token_id=2    ,		hidden_size=32    ,		intermediate_size=37    ,		layer_norm_eps=1E-05    ,		num_attention_heads=4    ,		num_hidden_layers=5    ,		pad_token_id=1    ,		vocab_size=1000    ,		)
        __a       =    CLIPTextModel(lowerCamelCase						)
        __a       =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)

        __a       =    {
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "safety_checker": None,
            "feature_extractor": None,
        }
        return components


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase=0						):
        if str(lowerCamelCase						).startswith("mps"						):
               __a       =    torch.manual_seed(lowerCamelCase						)
        else:
               __a       =    torch.Generator(device=lowerCamelCase						).manual_seed(lowerCamelCase						)
        __a       =    {
            "prompt": "A painting of a squirrel eating a burger",
            "generator": generator,
            "num_inference_steps": 2,
            "guidance_scale": 6.0,
            "output_type": "numpy",
        }
        return inputs


 def 						a__   ( self						):
        __a       =    "cpu"  # ensure determinism for the device-dependent torch.Generator

        __a       =    self.get_dummy_components()
        __a       =    StableDiffusionLDMaDPipeline(**lowerCamelCase						)
        __a       =    ldmad_pipe.to(lowerCamelCase						)
        ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase						)

        __a       =    self.get_dummy_inputs(lowerCamelCase						)
        __a       =    ldmad_pipe(**lowerCamelCase						)
        __a ,   __a       =    output.rgb, output.depth

        __a       =    rgb[0, -3:, -3:, -1]
        __a       =    depth[0, -3:, -1]

        assert rgb.shape == (1, 64, 64, 3)
        assert depth.shape == (1, 64, 64)

        __a       =    np.array(
            [0.3733_8176, 0.7_0247, 0.7420_3193, 0.5164_3604, 0.5825_6793, 0.6093_2136, 0.418_1095, 0.4835_5877, 0.4653_5262]						)
        __a       =    np.array([103.4_6727, 85.81_2004, 87.84_9236]						)

        assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb						).max() < 1E-2
        assert np.abs(image_slice_depth.flatten() - expected_slice_depth						).max() < 1E-2


 def 						a__   ( self						):
        __a       =    self.get_dummy_components()
        __a       =    StableDiffusionLDMaDPipeline(**lowerCamelCase						)
        __a       =    ldmad_pipe.to(lowerCamelCase						)
        ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase						)

        __a       =    self.get_dummy_inputs(lowerCamelCase						)
        __a       =    3 * [inputs["prompt"]]

        # forward
        __a       =    ldmad_pipe(**lowerCamelCase						)
        __a ,   __a       =    output.rgb, output.depth
        __a       =    rgb_slice_a[0, -3:, -3:, -1]
        __a       =    depth_slice_a[0, -3:, -1]

        __a       =    self.get_dummy_inputs(lowerCamelCase						)
        __a       =    3 * [inputs.pop("prompt"						)]

        __a       =    ldmad_pipe.tokenizer(
            lowerCamelCase    ,		padding="max_length"    ,		max_length=ldmad_pipe.tokenizer.model_max_length    ,		truncation=lowerCamelCase    ,		return_tensors="pt"    ,		)
        __a       =    text_inputs["input_ids"].to(lowerCamelCase						)

        __a       =    ldmad_pipe.text_encoder(lowerCamelCase						)[0]

        __a       =    prompt_embeds

        # forward
        __a       =    ldmad_pipe(**lowerCamelCase						)
        __a ,   __a       =    output.rgb, output.depth
        __a       =    rgb_slice_a[0, -3:, -3:, -1]
        __a       =    depth_slice_a[0, -3:, -1]

        assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()						).max() < 1E-4
        assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()						).max() < 1E-4






 def 						a__   ( self						):
        __a       =    "cpu"  # ensure determinism for the device-dependent torch.Generator
        __a       =    self.get_dummy_components()
        __a       =    PNDMScheduler(skip_prk_steps=lowerCamelCase						)
        __a       =    StableDiffusionLDMaDPipeline(**lowerCamelCase						)
        __a       =    ldmad_pipe.to(lowerCamelCase						)
        ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase						)

        __a       =    self.get_dummy_inputs(lowerCamelCase						)
        __a       =    "french fries"
        __a       =    ldmad_pipe(**lowerCamelCase    ,		negative_prompt=lowerCamelCase						)

        __a ,   __a       =    output.rgb, output.depth
        __a       =    rgb[0, -3:, -3:, -1]
        __a       =    depth[0, -3:, -1]

        assert rgb.shape == (1, 64, 64, 3)
        assert depth.shape == (1, 64, 64)

        __a       =    np.array(
            [0.3_7044, 0.7181_1503, 0.722_3251, 0.4860_3675, 0.563_8391, 0.636_4948, 0.4283_3704, 0.490_1315, 0.4792_6217]						)
        __a       =    np.array([107.8_4738, 84.6_2802, 89.96_2135]						)
        assert np.abs(rgb_slice.flatten() - expected_slice_rgb						).max() < 1E-2
        assert np.abs(depth_slice.flatten() - expected_slice_depth						).max() < 1E-2






@slow
@require_torch_gpu
class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase="cpu"    ,		lowerCamelCase=torch.floataa    ,		lowerCamelCase=0						):
        __a       =    torch.Generator(device=lowerCamelCase						).manual_seed(lowerCamelCase						)
        __a       =    np.random.RandomState(lowerCamelCase						).standard_normal((1, 4, 64, 64)						)
        __a       =    torch.from_numpy(lowerCamelCase						).to(device=lowerCamelCase    ,		dtype=lowerCamelCase						)
        __a       =    {
            "prompt": "a photograph of an astronaut riding a horse",
            "latents": latents,
            "generator": generator,
            "num_inference_steps": 3,
            "guidance_scale": 7.5,
            "output_type": "numpy",
        }
        return inputs






 def 						a__   ( self						):
        __a       =    StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"						)
        __a       =    ldmad_pipe.to(lowerCamelCase						)
        ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase						)

        __a       =    self.get_inputs(lowerCamelCase						)
        __a       =    ldmad_pipe(**lowerCamelCase						)
        __a ,   __a       =    output.rgb, output.depth
        __a       =    rgb[0, -3:, -3:, -1].flatten()
        __a       =    rgb[0, -3:, -1].flatten()

        assert rgb.shape == (1, 512, 512, 3)
        assert depth.shape == (1, 512, 512)

        __a       =    np.array(
            [0.5380_5465, 0.5670_7305, 0.548_6515, 0.5701_2236, 0.581_4511, 0.5625_3487, 0.5484_3014, 0.5509_2263, 0.645_9706]						)
        __a       =    np.array(
            [0.926_3781, 0.667_8672, 0.548_6515, 0.9220_2145, 0.6783_1135, 0.5625_3487, 0.924_1694, 0.755_1478, 0.645_9706]						)
        assert np.abs(rgb_slice - expected_slice_rgb						).max() < 3E-3
        assert np.abs(depth_slice - expected_slice_depth						).max() < 3E-3






@nightly
@require_torch_gpu
class  snake_case__      (       unittest.TestCase       ):


 def 						a__   ( self						):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase="cpu"    ,		lowerCamelCase=torch.floataa    ,		lowerCamelCase=0						):
        __a       =    torch.Generator(device=lowerCamelCase						).manual_seed(lowerCamelCase						)
        __a       =    np.random.RandomState(lowerCamelCase						).standard_normal((1, 4, 64, 64)						)
        __a       =    torch.from_numpy(lowerCamelCase						).to(device=lowerCamelCase    ,		dtype=lowerCamelCase						)
        __a       =    {
            "prompt": "a photograph of an astronaut riding a horse",
            "latents": latents,
            "generator": generator,
            "num_inference_steps": 50,
            "guidance_scale": 7.5,
            "output_type": "numpy",
        }
        return inputs


 def 						a__   ( self						):
        __a       =    StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d"						).to(lowerCamelCase						)
        ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase						)

        __a       =    self.get_inputs(lowerCamelCase						)
        __a       =    ldmad_pipe(**lowerCamelCase						)
        __a ,   __a       =    output.rgb, output.depth

        __a       =    0.49_5586
        __a       =    0.3379_5515
        __a       =    112.4_8518
        __a       =    98.48_9746
        assert np.abs(expected_rgb_mean - rgb.mean()						) < 1E-3
        assert np.abs(expected_rgb_std - rgb.std()						) < 1E-3
        assert np.abs(expected_depth_mean - depth.mean()						) < 1E-3
        assert np.abs(expected_depth_std - depth.std()						) < 1E-3






 def 						a__   ( self						):
        __a       =    StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c"						).to(lowerCamelCase						)
        ldmad_pipe.set_progress_bar_config(disable=lowerCamelCase						)

        __a       =    self.get_inputs(lowerCamelCase						)
        __a       =    ldmad_pipe(**lowerCamelCase						)
        __a ,   __a       =    output.rgb, output.depth

        __a       =    0.419_4127
        __a       =    0.3537_5586
        __a       =    0.563_8502
        __a       =    0.3468_6103

        assert rgb.shape == (1, 512, 512, 3)
        assert depth.shape == (1, 512, 512, 1)
        assert np.abs(expected_rgb_mean - rgb.mean()						) < 1E-3
        assert np.abs(expected_rgb_std - rgb.std()						) < 1E-3
        assert np.abs(expected_depth_mean - depth.mean()						) < 1E-3
        assert np.abs(expected_depth_std - depth.std()						) < 1E-3

"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
   import PIL

SCREAMING_SNAKE_CASE__:Tuple									=			logging.get_logger(__name__)



def 	_lowerCamelCase(	a				):
       if isinstance(a   ,    (list, tuple)				) and isinstance(videos[0]   ,    (list, tuple)				) and is_valid_image(videos[0][0]				):
              return videos

       elif isinstance(a   ,    (list, tuple)				) and is_valid_image(videos[0]				):
              return [videos]

       elif is_valid_image(a				):
              return [[videos]]

       raise ValueError(F"Could not make batched video from {videos}"				)






class  snake_case__      (       snake_case_       ):
 _snake_case       :					List[Any]							  =						["""pixel_values"""]


 def __init__( self    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = PILImageResampling.BILINEAR    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = True    ,		lowerCamelCase = 1 / 255    ,		lowerCamelCase = True    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        super().__init__(**lowerCamelCase						)
        __a       =    size if size is not None else {"shortest_edge": 256}
        __a       =    get_size_dict(lowerCamelCase    ,		default_to_square=lowerCamelCase						)
        __a       =    crop_size if crop_size is not None else {"height": 224, "width": 224}
        __a       =    get_size_dict(lowerCamelCase    ,		param_name="crop_size"						)

        __a       =    do_resize
        __a       =    size
        __a       =    do_center_crop
        __a       =    crop_size
        __a       =    resample
        __a       =    do_rescale
        __a       =    rescale_factor
        __a       =    offset
        __a       =    do_normalize
        __a       =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __a       =    image_std if image_std is not None else IMAGENET_STANDARD_STD


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = PILImageResampling.BILINEAR    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        __a       =    get_size_dict(lowerCamelCase    ,		default_to_square=lowerCamelCase						)
        if "shortest_edge" in size:
               __a       =    get_resize_output_image_size(lowerCamelCase    ,		size["shortest_edge"]    ,		default_to_square=lowerCamelCase						)
        elif "height" in size and "width" in size:
               __a       =    (size["height"], size["width"])
        else:
               raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"						)
        return resize(lowerCamelCase    ,		size=lowerCamelCase    ,		resample=lowerCamelCase    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        __a       =    get_size_dict(lowerCamelCase						)
        if "height" not in size or "width" not in size:
               raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}"						)
        return center_crop(lowerCamelCase    ,		size=(size["height"], size["width"])    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        __a       =    image.astype(np.floataa						)
        if offset:
               __a       =    image - (scale / 2)
        return rescale(lowerCamelCase    ,		scale=lowerCamelCase    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		**lowerCamelCase    ,		):
        return normalize(lowerCamelCase    ,		mean=lowerCamelCase    ,		std=lowerCamelCase    ,		data_format=lowerCamelCase    ,		**lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = ChannelDimension.FIRST    ,		):
        if do_resize and size is None or resample is None:
               raise ValueError("Size and resample must be specified if do_resize is True."						)

        if do_center_crop and crop_size is None:
               raise ValueError("Crop size must be specified if do_center_crop is True."						)

        if do_rescale and rescale_factor is None:
               raise ValueError("Rescale factor must be specified if do_rescale is True."						)

        if do_normalize and (image_mean is None or image_std is None):
               raise ValueError("Image mean and std must be specified if do_normalize is True."						)

        if offset and not do_rescale:
               raise ValueError("For offset, do_rescale must also be set to True."						)

        # All transformations expect numpy arrays.
        __a       =    to_numpy_array(lowerCamelCase						)

        if do_resize:
               __a       =    self.resize(image=lowerCamelCase    ,		size=lowerCamelCase    ,		resample=lowerCamelCase						)

        if do_center_crop:
               __a       =    self.center_crop(lowerCamelCase    ,		size=lowerCamelCase						)

        if do_rescale:
               __a       =    self.rescale(image=lowerCamelCase    ,		scale=lowerCamelCase    ,		offset=lowerCamelCase						)

        if do_normalize:
               __a       =    self.normalize(image=lowerCamelCase    ,		mean=lowerCamelCase    ,		std=lowerCamelCase						)

        __a       =    to_channel_dimension_format(lowerCamelCase    ,		lowerCamelCase						)
        return image






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = ChannelDimension.FIRST    ,		**lowerCamelCase    ,		):
        __a       =    do_resize if do_resize is not None else self.do_resize
        __a       =    resample if resample is not None else self.resample
        __a       =    do_center_crop if do_center_crop is not None else self.do_center_crop
        __a       =    do_rescale if do_rescale is not None else self.do_rescale
        __a       =    rescale_factor if rescale_factor is not None else self.rescale_factor
        __a       =    offset if offset is not None else self.offset
        __a       =    do_normalize if do_normalize is not None else self.do_normalize
        __a       =    image_mean if image_mean is not None else self.image_mean
        __a       =    image_std if image_std is not None else self.image_std

        __a       =    size if size is not None else self.size
        __a       =    get_size_dict(lowerCamelCase    ,		default_to_square=lowerCamelCase						)
        __a       =    crop_size if crop_size is not None else self.crop_size
        __a       =    get_size_dict(lowerCamelCase    ,		param_name="crop_size"						)

        if not valid_images(lowerCamelCase						):
               raise ValueError(
                   "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                   "torch.Tensor, tf.Tensor or jax.ndarray."						)

        __a       =    make_batched(lowerCamelCase						)

        __a       =    [
            [
                self._preprocess_image(
                    image=lowerCamelCase    ,		do_resize=lowerCamelCase    ,		size=lowerCamelCase    ,		resample=lowerCamelCase    ,		do_center_crop=lowerCamelCase    ,		crop_size=lowerCamelCase    ,		do_rescale=lowerCamelCase    ,		rescale_factor=lowerCamelCase    ,		offset=lowerCamelCase    ,		do_normalize=lowerCamelCase    ,		image_mean=lowerCamelCase    ,		image_std=lowerCamelCase    ,		data_format=lowerCamelCase    ,		)
                for img in video
            ]
            for video in videos
        ]

        __a       =    {"pixel_values": videos}
        return BatchFeature(data=lowerCamelCase    ,		tensor_type=lowerCamelCase						)
